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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
ISIE focuses on advancements in knowledge, new methods, and technologies relevant to industrial electronics, along with their applications and future developments.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.
2011 The 14th International Symposium on Wireless Personal Multimedia Communications (WPMC), 2011
This paper proposes an alternative method for an indoor event detection scheme that realizes immunity against co-channel interference by exploiting the cyclostationarity of the desired signal. The previous indoor event detection scheme  can detect events such as home or office intrusion by using signal subspace spanned by an eigenvector obtained by an array antenna, and delivers superior performance compared ...
2010 IEEE Asia Pacific Conference on Circuits and Systems, 2010
Gait studies in sports and rehabilitation may benefit from online gait event detection algorithms for use in event-dependant feedback strategies. Event- dependant feedback systems may further benefit from durable, lightweight, low cost sensors for gait event detection. In this regard, this study describes the development and feasibility evaluation of an online gait event detection system using inertial sensor technology for ...
2015 International Conference on Cloud Computing and Big Data (CCBD), 2015
Online event detection techniques are usually used in single data source. This paper analyzes event detection in the perspective of multiple data sources, combining news reports and microblogs. Detect events from news, combining microblogs to do event monitoring and early warning. Also improve feature selection methods for multiple data sources event detection. Finally, the methods are applied to the detection ...
IEEE Transactions on Multimedia, 2016
We consider the problem of event detection in video for scenarios where only a few, or even zero, examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pretrained concept detectors. Different from existing work, we propose a new semantic video representation that is based ...
2012 Complexity in Engineering (COMPENG). Proceedings, 2012
Since the events of 9/11 2001 in the US the world public awareness to possible terrorist attacks on water supply systems has increased dramatically, causing the security of drinking water distribution systems to become a major concern around the globe. Among the different threats, a deliberate chemical or biological contaminant injection is the most difficult to address, both as a ...
Contactless Wireless Sensing - Shyam Gollakota - IEEE EMBS at NIH, 2019
An FPGA-Quantum Annealer Hybrid System for Wide-Band RF Detection - IEEE Rebooting Computing 2017
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
Implantable, Insertable and Wearable Micro-optical Devices for Early Detection of Cancer - Plenary Speaker, Christopher Contag - IPC 2018
Critical use cases for video capturing systems in autonomous driving applications
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Developing Automated Analysis Tools for Space/Time Sidechannel Detection - IEEE SecDev 2016
IEEE Medal for Environmental and Safety Technologies - Jerome Faist and Frank K. Tittell - 2018 IEEE Honors Ceremony
An IEEE IPC Special Session with X. Chen from Nokia Bell Labs
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
ASC-2014 SQUIDs 50th Anniversary: 4 of 6 - Keiji Enpuku
Low Power Image Recognition: The Challenge Continues
A Recurrent Crossbar of Memristive Nanodevices Implements Online Novelty Detection - Christopher Bennett: 2016 International Conference on Rebooting Computing
Welcome to ICRA 2015: Robot Challenges
IEEE Highlight: Electronic Nose: Diagnosing Cancer Through Smell
Silicon THz: an Opportunity for Innovation
Experience ICRA 2015: Robot Challenges
Engineering Meets Biology in Tech News
This paper proposes an alternative method for an indoor event detection scheme that realizes immunity against co-channel interference by exploiting the cyclostationarity of the desired signal. The previous indoor event detection scheme  can detect events such as home or office intrusion by using signal subspace spanned by an eigenvector obtained by an array antenna, and delivers superior performance compared with conventional event detection methods based on received signal strengths (RSS). Similar to the conventional methods, however, the signal subspace-based sometimes suffers from interference in the same frequency band. The method proposed in this paper exploits the cyclostationarity of communication signals to distinguish the desired signal from the interference that impinges on an array antenna, and suppresses noise and interfering signals without major hardware design changes. The proposed algorithm is computationally simple and offers good detection performance. The effectiveness of the proposed method is verified through numerical examples. Finally, an evaluation equipment of the event detection system is introduced to realize the real-time event detection.
Gait studies in sports and rehabilitation may benefit from online gait event detection algorithms for use in event-dependant feedback strategies. Event- dependant feedback systems may further benefit from durable, lightweight, low cost sensors for gait event detection. In this regard, this study describes the development and feasibility evaluation of an online gait event detection system using inertial sensor technology for the identification of Heel Strike (HS) and Toe Off (TO) events during treadmill running. Custom developed system software performs the online data acquisition, processing, graphical representations of lower extremity kinematics and online gait event detection. For increased robustness, a Finite State Controller architecture is employed for continuous detections of HS and TO during running. Pilot tests conducted with 7 healthy subjects during treadmill running verified the accuracy of gait event detection with mean timing errors of 14ms for HS and 27ms for TO compared to normative values. The effectiveness and robustness of gait event detection is promising signifying the use of the system for triggering event- dependant feedback during running gait retraining.
Online event detection techniques are usually used in single data source. This paper analyzes event detection in the perspective of multiple data sources, combining news reports and microblogs. Detect events from news, combining microblogs to do event monitoring and early warning. Also improve feature selection methods for multiple data sources event detection. Finally, the methods are applied to the detection of food safety events and the results of the research show that event detection with multiple data sources is meaningful and valuable.
We consider the problem of event detection in video for scenarios where only a few, or even zero, examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pretrained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness, and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.
Since the events of 9/11 2001 in the US the world public awareness to possible terrorist attacks on water supply systems has increased dramatically, causing the security of drinking water distribution systems to become a major concern around the globe. Among the different threats, a deliberate chemical or biological contaminant injection is the most difficult to address, both as a consequence of the uncertainty surrounding the type of the injected contaminant and its consequences, as well as the uncertainty of location and time of the injection. In principle, a pollutant can be injected at any water distribution system connection (node) using a pump or a mobile pressurized tank. Although backflow preventers provide an obstacle to such actions, they do not exist at all connections, and at some might not be functional. This paper describes recent effort modeling of Avi Ostfeld's research team on water distribution systems event detection. The basic event detection framework is entitled AEDA (Aquatic Event Detection Algorithm) which utilizes Artificial Neural Networks (ANNs) for studying the interactions between multivariate water quality parameters and detecting possible outliers. Other layers on top of AEDA explore tradeoffs among contamination event parameters and improving its performance capabilities. Those and AEDA are reviewed in this paper.
Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt the multi-label classification technique to perform the polyphonic acoustic event detection with a global threshold to detect the active acoustic events. However, the manually labeled boundaries are error-prone and cannot always be accurate, especially when the frame length is too short to be accurately labeled by human annotators. To deal with this, a confidence is assigned to each frame and acoustic event detection is performed using a multi-variable regression approach in this paper. Experimental results on the latest TUT sound event 2017 database of polyphonic events demonstrate the superior performance of the proposed approach compared to the multi-label classification based AED method.
Composite event detection is one of fundamental tasks for wireless sensor networks. In existing approaches, typically, a routing tree is used to enable information exchange among sensor nodes and collaborative detection of composite events. However, such a tree is not optimal in terms of energy efficiency, because the relations included in composite events have not been fully utilized. In this letter, we propose a new type of routing tree called event detection tree (EDT) to achieve energy-efficient composite event detection. EDT reduces the amount of data to be transmitted by aggregating data in to events, at the cost of an increased distance in the data transmission to achieve such aggregations. EDT achieves a tradeoff of them to minimize the overall energy consumption. Simulation results show that our approach outperforms existing approaches and yields energy savings of up to 20%.
Event detection over RFID event streams is one of the most important applications of RFID based monitoring systems. Event detection over data stream extracts meaningful complex patterns from high volume, fast speed and real time raw event streams. This research aims to detect RFID event patterns by introducing context information to reduce event instances effectively. Context of an RFID application is modeled and merged into event query optimization framework. Context information is further applied into evaluation of pattern detection. Experimental results show that context information can optimize complex event detection process both on CPU time and memory consumption.
Once an event occurs, usually there are a large number of online news to be released. How to quickly and accurately detect the hot events from the huge amount of online news is the focus and hotspot. Event detection and tracking technology is as a key technology to solve this problem. In this paper, we propose an approach to detect hot events from the online news stream in a timely manner and track the hot events. Based on the idea of single-pass clustering algorithm, this approach address the weight of keywords and proposes a new method to calculate similarity among news to track event. Through the analysis of the experimental results, we can find that this algorithm has a good effect on hot event detection.
This paper presents research done towards a robust real-time social network text stream event detection system that combines text stream mining and network analysis methods. It presents the current state-of-the-art systems, algorithms, and methodologies to perform event detection in streaming environments: If from the point of view of a natural language processing, text mining, and unsupervised learning the problem of detecting events in unbounded text streams is hard, dealing with dynamic networks with millions of nodes and edges is not also an easy task. Presented contributions and research directions are based on the premise that the precision and accuracy of an event detection algorithm could be improved by considering network properties of the social network when events happen. Network analysis algorithms, specifically algorithms to perform dynamic community detection, community identification and community tracking can be used to extract knowledge from users relations and interactions helping in the task of unveiling and detecting new or unforeseen events.
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Neutrino Physics - Postdoctoral Researcher
Lawrence Livermore National Laboratory
Nuclear & Particle Physics - Postdoctoral Researcher
Lawrence Livermore National Laboratory
Neutrino Physics nEXO - Postdoctoral Researcher
Lawrence Livermore National Laboratory